Improvments in and relating to image classification using retinal ganglion cell modelling

ABSTRACT

A method of processing a digital image for use by a digital image classifier comprises: processing the digital image with computational models of a retinal ganglion cell (RGC) to produce sets of digital image features; and combining the sets of digital image features to produce a multi-channel retina model image. The method may be used in digital image classification and in training a digital image classifier. The creation and use of multi-channel retina model images improves the ability to detect pertinent image features during image classification and so improves the overall classification process.

FIELD OF THE INVENTION

This invention relates to artificial vision. The invention relatesparticularly to artificial vision involving modelling retinal ganglioncells (RGC).

BACKGROUND TO THE INVENTION

Biological visual processing begins within the retina, which is acomplex, networked organisation of cells comprising photoreceptors,horizontal cells, bipolar cells, amacrine cells and retinal ganglioncells (RGCs). The RGCs, which are a type of sensory neuron, typicallyinclude a plurality of the retina's photoreceptors. A typical retina hasapproximately 1 million RGCs, each pooling a signal from multiplephotoreceptors that define a spatial area known as a receptive field(RF). Light, upon entering the eye, is focused onto the photoreceptorlayer effecting a change in each cell's potential and forming a signalthat is communicated through the various inter-processing layers to theRGCs. In response to the visual stimulus received by the photoreceptors,the RGCs generate electrophysiological output signals known as actionpotentials (or spikes), which are transmitted via synaptic connectionsto the visual cortex for higher processing.

Modelling the input/output relationship of RGCs is of interest sinceemploying biologically derived aspects to artificial visual processingcan out-perform various machine vision techniques in terms of power,speed and performance. An important step towards developing artificialvision is therefore to develop computational models of the RGCs that aimto replicate biological processing. Conventional artificial visiontechnologies are based on the theory of biological processing ratherthan on actual biological cells. However, a complete theoreticalunderstanding of the encoding mechanisms and connectivity of retinalganglion cells is still unknown and so theoretical based computationalmodels are compromised.

It would be desirable to provide improved artificial vision systems.

SUMMARY OF THE INVENTION

From a first aspect the invention provides a method of processing adigital image for use by a digital image classifier, the methodcomprising: processing said digital image with each of a plurality ofcomputational models of a retinal ganglion cell (RGC) to produce arespective set of digital image features; and combining said sets ofdigital image features to produce a multi-channel retina model image.Advantageously, at least some of, preferably all of, said RGCcomputational models are associated with a respective differentreceptive field (RF) of a retina.

Preferably, said processing involves processing a respective part ofsaid digital image with a respective one of said RGC computationalmodels. Said respective part of said digital image may correspond withthe respective RF associated with the respective RGC computationalmodel.

Preferably, said processing involves convolving said digital image witheach of said RGC computational models.

Preferably, at least some of, preferably all of, said respectivereceptive fields (RFs) are overlapping with at least one other of saidrespective receptive fields (RFs).

Preferably, at least some of, preferably all of, said respectivereceptive fields (RFs) have a different size and/or a different shape.

Advantageously, each RGC computational model is calculated from adataset comprising input data and corresponding output data, wherein theinput data comprises image data and the output data represents theresponse of an RGC to the image data. Preferably, at least some of saidRGC computational models are derived from a different RGC and/or adifferent type of RGC. Optionally, said input data comprises datarepresenting a sequence of images, preferably a sequence of Gaussianimages or a sequence of checkerboard images. Optionally, each RGC modelis calculated from said dataset by reverse correlation and/or by machinelearning. Preferably, each RGC model comprises a spike-triggered average(STA) derived from said dataset.

Preferably, each RGC computational model corresponds to a differentspatial portion of said image, and wherein, preferably, said pluralityof RGC computational models collectively correspond to the whole image.Preferably, at least some of, preferably all of, said RGC computationalmodels correspond to a spatial portion of the image that overlaps withthe spatial image portion corresponding to at least one other of saidRGC computational models.

From a second aspect the invention provides a method of digital imageclassification comprising processing a digital image using the method ofthe first aspect of the invention, and providing said multi-channelretina model image to a digital image classifier.

From a third aspect the invention provides a method of training adigital image classifier, the method comprising processing digitalimages using the method of the first aspect of the invention, andproviding the corresponding multi-channel retina model images to thedigital image classifier.

Typically, said digital image classifier comprises an artificial neuralnetwork (ANN).

From a fourth aspect the invention provides a method of classifying adigital image using a digital image classifier, said method includingproviding said digital image classifier with a plurality ofcomputational models of a retinal ganglion cell (RGC), and processingsaid digital images in accordance with the method of the first aspect ofthe invention. The digital image classifier may comprise an artificialneural network (ANN), and providing said digital image classifier with aplurality of computational models of a retinal ganglion cell (RGC) mayinvolve providing said RGC models in a first layer of said ANN.

Optionally, said digital image classifier comprises a convolutionalneural network (CNN), and wherein providing said digital imageclassifier with a plurality of computational models of a retinalganglion cell (RGC) involves providing said RGC models in aconvolutional base of said CNN.

From a fifth aspect the invention provides a digital image processor foruse with a digital image classifier, the digital image processorcomprising means for performing the method of the first aspect of theinvention. The means for performing the method of the first aspect ofthe invention may comprise hardware, for example a suitably configuredintegrated circuit such as an ASIC or FPGA, and/or one or moreprocessors (e.g. microprocessor(s)) or computing device(s) programmedwith suitably configured computer software.

From a sixth aspect the invention provides a digital image classifiercomprising a digital image processor according to the fifth aspect ofthe invention. The digital image classifier may comprise means forperforming the method of any one or more of the second, third and fourthaspects of the invention, wherein said means may comprise hardware, forexample a suitably configured integrated circuit such as an ASIC orFPGA, and/or one or more processors (e.g. microprocessor(s)) orcomputing device(s) programmed with suitably configured computersoftware.

From one aspect the invention provides an artificial vision encoderbased on recordings of real retinal ganglion cells.

In preferred embodiments, recordings of how individual retinal ganglioncells respond to artificial natural image stimulation are used to modelRGC behaviour using system identification and computational modelling.The resulting model is transparent and permits individual parametersanalysis. Moreover, the model encapsulates the processing within theRGC's neural structure, bypassing the need ^(.)for completeunderstanding of biological system but maintaining the processingability. In addition, low processing requirements make hardwareimplementations quick and low cost.

Advantageously, a plurality of RGC models used to encode an image toproduce a corresponding multi-channel retina model image. The creationand use of multi-channel retina model images improves the ability todetect pertinent image features during image classification and soimproves the overall classification process.

Further advantageous aspects of the invention will be apparent to thoseordinarily skilled in the art upon review of the following descriptionof a specific embodiment and with reference to the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

An embodiment of the invention is now described by way of example andwith reference to the accompanying drawings in which:

FIG. 1 is a flow chart illustrating a preferred method of obtaining adata set of stimulation images and corresponding RGC responses;

FIG. 2A shows a set of uniform intensity temporal images;

FIG. 2B shows a set of spatio-temporal checkerboard images;

FIG. 3 is a flow chart illustrating a preferred method of obtaining RGCmodels;

FIG. 4 is a schematic diagram illustrating the preferred method ofgenerating a multi-channel retina model image;

FIG. 5 is a schematic diagram illustrating a machine learning systemreceiving a multi-channel retina model image in accordance with oneaspect of the invention; and

FIG. 6 is a schematic diagram of a convolutional neural networkembodying another aspect of the invention.

DETAILED DESCRIPTION OF THE DRAWINGS

Each RGC produces a physiological output signal in response to a visualstimulus. The output signal may be referred to as the response of theRGC.

Referring now to FIG. 1 of the drawings, a preferred method of obtaininga representation of individual RGC responses to input digital images isdescribed. At 101 a retina is isolated from an animal, for example anaxolotl tiger salamander, and placed on a suitable transducer,conveniently a multi-electrode array, that is capable of detectingoutput signals from the RGCs and producing corresponding output signals,typically electrical, that can be recorded. The isolated retina may beplaced ganglion cell side down on a planar multi-electrode array that ispreferably submersed in a chemical solution to prolong extracellularrecordings.

At 102, the isolated retina is visually stimulated using a sequence ofimages, which may comprise artificial and/or natural images. For exampleartificial images may comprise a sequence of Gaussian white noise imagesor a sequence of binary checkerboard patterns, while natural images maycomprise a sequence of images of real-world scenes. The images may beprojected onto the retina in any convenient manner, for example using aminiature organic light-emitting diode (OLED) monitor. One or more lensmay be used to de-magnify the images and focus them on the photoreceptorlayer of the retina.

At 103, the output signals, or neural responses, of the RGCs arerecorded. The response for each cell is represented by a temporal seriesof spikes known as a spike train, in which the RGC-processed informationfrom the visual stimulus is considered to be encoded. Somepre-processing of the RGC responses may be performed, for exampleincluding any one or more of: conventional spike sorting for removingnoise and spurious elements; cluster analysis of spike shapes;determining spike occurrence time relative to the beginning of thevisual stimulation of step 102. Optionally, each spike train istransformed into a corresponding spike rate. This transformation may beperformed in any conventional manner, for example by sliding amathematical window function along the neural responses or alternativelyby averaging responses over multiple trials of the same stimuli.

In any case, at 104, data comprising a set of RGC neural responses (i.e.respective data indicative of the respective response of each of aplurality of RGCs) together with the set of images (i.e. respectiveimage data) that elicited those responses is obtained. The RGC responsedata is time-correlated with the respective image data.

After the neural recording is complete, the next stage is deriving acomputational model for each RGC for which a respective neural responsehas been recorded, which in preferred embodiments comprises calculatinga spike-triggered average (STA) for each RGC. The STA is a means ofcharacterizing the response of an ROC using the spikes emitted by theRGC in response to a time-varying stimulus. The STA provides an estimateof the RGC's receptive field (RF). Calculating an STA, which issometimes referred to as spike-triggered averaging, may be performedusing any conventional technique including reverse correlation or whitenoise analysis, or by machine learning. The STA is the average stimuluspreceding an output spike. To compute the STA, the stimuli in a timewindow preceding each spike may be extracted and averaged. Reversecorrelation is a known technique for determining how sensory neurons sumsignals from different locations in theft receptive fields, and also howthey sum stimuli that they receive at different times, to generate aresponse.

The RF is the spatial area of photoreceptors which contribute to an RGCeliciting a response, and may also be referred to as the region of thesensory space (comprising the relevant photoreceptors) in which visualstimulus causes the cell to generate an output signal, i.e. triggers thecell to fire. In reality, the shape of an RF is irregular though it maybe approximated to be either circular or elliptical, for example using a2D Gaussian spatial profile. Identifying a RF in terms of its shape,size and location is part of formulating a model that describes therelationship between stimulus and response.

To calculate the STA, and so to determine the size, location and shapeof the RF, the retina is stimulated with artificial stimuli and thecorrelation between the stimulus and output response is analysed.Alternatively, the natural images may be used as a stimulus. By way ofexample, the RF may be determined using techniques disclosed in“Dependence of the retinal Ganglion cell's responses on local texturesof natural scenes” by Cao X et al, Journal of Vision, 2011 May 20;11(6). pii: 11. doi: 10.1167/11.6.11. In any case, visual stimuli areused to compute the spatio-temporal STA which provides the RGC'sspatio-temporal response. The spatial area identified using this processis subsequently used to determine what region of the image andcorresponding pixel intensities are used in the modelling process.

In order to determine the STA, recordings of RGC responses may be madeby visually stimulating the retina using temporal and/or spatio-temporalGaussian white noise sequences, or any other suitable image sequence.Examples of suitable stimuli are presented in FIGS. 2A and 2B, whichshow a temporal sequence and a spatio-temporal sequence respectively.Each sequence comprises multiple images that are presented sequentiallyto the retina.

FIG. 2A shows a set of uniform intensity temporal images drawn randomlyfrom a normal distributed Gaussian white noise sequence, typically withzero mean and a standard deviation of 1. The sequence may be generatedusing the gasdev( )function from the Numerical Recipes library. Suchtemporal sequences may be used for full field illumination of theretina, where all pixels within each image are illuminated with the sameuniform light intensity, thus no spatial arrangement is observable. Thisis referred to as Full Field Flicker (FFF) and is the least complex formof artificial stimulus used. RGC models derived under these conditionswould be considered a subset of the real neural model as they onlyconsider the temporal components.

The image sequence of FIG. 2B extends the stimulus input range tocomprise a spatio-temporal input by introducing a binary checkerboardpattern, or Checker-Board Flicker (CBF). CBF extends the complexity ofthe input due to an additional spatial component varying randomly acrosstime. In terms of modelling, it allows the incorporation of localspatial summation of information within the complete RF whereas FFF doesnot. Each binary checker may for example have a resolution of 10×10pixels onscreen, and may be drawn from a random sequence generated usingthe gasdev( ) function with, for example, an assigned value of either−0.5 or 0.5, independent of neighbouring checkers. The stimulus valuesof the spatio-temporal input that contribute to the RGC eliciting aspike are used to determine, or estimate, a spatio-temporal receptivefield (STRF) for the respective RGC. In this connection it is noted thatthe STA is an approximation of the STRF and may be obtained from theSTRF by any suitable post-processing. Determination of the STRF, or RF,may be performed using any convenient conventional method, for exampleusing reverse-correlation or machine learning.

Typically, the STRF represents both space and time, while an RFtypically represents one or other of space and time. Using a sequencesuch as that shown in FIG. 2A, a temporal RF can be generated. An STRFcan be generated from a sequence such as that shown in FIG. 2B.Typically, sequences of the type shown in one or other of FIGS. 2A and2B may be used to produce an RF or an STRF depending on what type ofmodel it is desired to obtain. In preferred embodiments, the RF or STRFis used to obtain the cell model. Optionally, the STA may represent thecell model.

FIG. 3 illustrates how the recorded neural response data and thecorresponding input image data can be used to generate a computationalmodel of the RGC. A dataset of images and corresponding neuralrecordings is obtained (301). At 302 and 303, machine learning orreverse correlation methods are used to create the RGC models from thedata. In preferred embodiments, the dataset obtained at step 104 of FIG.1 is used at step 301. Image data of the type shown in FIG. 2A or 2B maybe used to generate the neural responses in the dataset of FIG. 1 . Togenerate the RGC models, the image data may be used as the input and theneural responses as the output.

After the STA is determined an input-output dataset is obtained, wherethe input corresponds to the pixel intensities within the cell'sreceptive field region and the output is the spike rate (or otherrepresentation of the cell's output response) which has been estimatedfrom the neural response.

Each RGC computational model M can be represented as:

M=f(In, y)   (1)

where M is the RGC model, I is the input image data, index n=1, . .. . ,m where m is the number of frames in the data set, y is the respectiveresponse, or spike, output, and f represents a computational function.The obtained STRF is decomposed into corresponding spatial and temporalcomponents, for example using Singular Value Decomposition (SVD) or anyother suitable conventional mathematical method, which may berepresented by Ms and Mt respectively. In preferred embodiments, thespatial STA is obtained from the STRF, and the spatial STA is used asthe model Ms.

In preferred embodiments, the respective RGC spatial component Ms foreach RGC is used as a computational model of the RGC.

As described above, in some embodiments, reverse correlation may beperformed using Gaussian or checkerboard stimulus to produce the STA,and that the spatial part of the STA may serve as the cell model.Alternatively, the STA is computed, optionally using reversecorrelation, resulting in an input-output dataset which is subsequentlysubjected to machine learning techniques (for example the NARMAX systemidentification approach and the Self-Organising Fuzzy Neural Network(SOFNN) approach) in order to produce the cell model. This isillustrated in FIG. 3 which indicates that machine learning or reversecorrelation may be used to produce the cell model. Typically, reversecorrelation is used to produce an input data set (in particular areduced input data set), and then the cell model may be generated usingmachine learning or reverse correlation.

Once the computational models M have been obtained they can be used toconvert, or encode, digital images or image sequences (such as videoclips or movies) into artificial ganglion cell image features. Inpreferred embodiments, this process involves taking one or more spatialareas of an image that corresponds to the receptive field RF of therespective RGC and using this spatial area as input into the respectiveRGC model to produce an output comprising RGC digital image features,i.e. corresponding to features produced by the respective modelled RGC.The whole image may be input to each RGC model, in response to whicheach model is only be responsive to the parts of the image correspondingto its RF.

The digital image features may be used in machine learning systemsduring the training of the machine learning system and/or duringclassification of digital images by a trained machine learning system.In preferred embodiments, during training and/or classification, imagesare encoded as ganglion cell features using RGC models. Optionally,during training and/or classification, images are processed by multipleRGC models and the resultant ganglion cell features are provided asinput to the machine learning system, i.e. images are processed by theRGC models as a pre-processing step before image data is provided to themachine learning system. The resulting trained machine learning systemcan subsequently be used on input images to detect the respectivefeatures. Alternatively or in addition the RGC models can beincorporated into the machine learning system to process images providedto the system.

Referring now to FIG. 4 of the drawings, a preferred embodiment isdescribed in which a plurality n of RGC models M1 to Mn are used toprocess, or encode, an image G (or more particularly data representingthe image) to produce a multi-channel (or N-channel) retina model imageN_(G). At least some of, and preferably all of, the n RGC models M1 toMn are of a different type (e.g. associated with a different RF and/or adifferent RGC and/or a different type of RGC). Each RGC model M1 to Mn,processes the image G to produce a respective part, or channel, N_(G1),to N_(gn) of the multi-channel retina model image N_(G). Each partN_(G1) to N_(Gn) of the retina model image N_(G) encodes different typesof visual features within the image G. Accordingly, each part N_(G1) toN_(Gn) of the retina model image N_(G) comprises a respective set ofimage features extracted from the image G. The retina model image N_(G)may be provided as an input to a machine learning system in order totrain the machine learning system to perform image classification.Alternatively, images that are to be classified by a trained machinelearning system may be pre-processed to produce a correspondingmulti-channel retina model image N_(G), the retina model image N_(G)then being provided to the machine learning system for classification.Each RGC model may be said to produce a respective channel, or part, ofthe retina model image N_(G) in that the respective output of each RGCmodel corresponds to different types of extracted digital imagefeatures, and wherein each type may be determined by the receptive field(RF) associated with the respective RGC model. The combined outputs ofthe RGC models may be said to comprise a retina model image in that,collectively, the respective RF with which the RGC models are associatedcorrespond to different ganglion cells of a retina, in particulardifferent RFs formed from the respective RGCs within a retina. Therespective output from each RGC model, when the image is processed,comprises a respective set of digital image features. The creation anduse of multi-channel (or N-channel) retina model images N_(G) improvesthe ability to detect pertinent image features during imageclassification and so improves the overall classification process.

In preferred embodiments, and as illustrated in equation (2), theretinal model image N_(G) is obtained by convolving the RGC models Mwith the image data G and combining the outputs to form the N-channelretinal model image, where n is the number of RGC models used:

N_(G)=M⊗G   (2)

In preferred embodiment, overlapping convolution is used when convolvingthe RGC models M with the image data G.

In preferred embodiments, the image G is an intensity image wherein eachpixel is represented by a single intensity value. Other conventionaldigital representations of the image G may be used, e.g. comprisingcolour or multispectral channel(s).

In preferred embodiments, selecting RGC models of different types forproducing the retina model image may be performed manually. The selectedmodels preferably correspond to cells where the receptive fields havedifferent size and shape. The models are preferably applied in anoverlapping manner across the images.

FIG. 5 illustrates how retina model images N_(G) can be used with amachine learning system MLS in a training mode to train the machinelearning system MLS, or in a classification mode in order to classifythe image used to create the retina model image N_(G). The operation ofthe machine learning system MLS can be represented mathematically as:

T=D(N _(G))   (3)

where T is the target output and D is the function implemented bymachine learning system MLS.

The machine learning system MLS may comprise any conventional machinelearning system but typically comprises an artificial neutral networkANN, which may take any conventional form. The ANN typically comprises aseries of network layers, including an input layer, an output layer andone or more intermediate layers. Each layer comprises one or morenetwork nodes, the nodes of each layer being connected to one or morenodes of the next layer by one or more weighted connections. Inpreferred embodiments, the ANN comprises a deep learning network. TheMLS of FIG. 5 may be described as a digital image classifier.

In the training mode, multiple N -channel retina model images N_(G) areused to train the machine learning system MLS, and in particular theANN. Using retina model images N_(G) to train the ANN rather than rawimages to train the ANN improves the ANN's ability to detect pertinentimage features during image classification and so to improve theclassification process.

In the classification mode, each input image is first converted to itscorresponding N-channel retinal model image N_(G) prior to being inputto the machine learning system MLS, and more particularly to the ANN.This improves the ANN's ability to detect pertinent image featuresduring image classification and so to improve the classificationprocess.

The images to be classified are converted into N-channel retina imagespreferably using the same RGC models as were used for the trainingimages.

In alternative embodiments, the RGC models M1 to Mn may be incorporatedinto the machine learning system MLS, and in particular into the ANN. Insuch embodiments, the ANN preferably comprises a convolutional neuralnetwork (CNN). With reference to FIG. 6 , a CNN comprises aconvolutional base 60 and a trainable classifier 62. The trainableclassifier 62 may take any conventional form, typically comprising atrainable multi-layer ANN. The convolutional base 60 typically comprisesone or more convolutional layer and one or more pooling layer. Theconvolutional base 60 is configured to extract image features from aninput image. The classifier 62 is configured to classify the image fromthe image features extracted by the convolutional base 60. The CNN ofFIG. 6 may be described as a digital image classifier.

CNNs are suitable for use in transfer learning applications. Transferlearning is a popular method in computer vision because it allowsaccurate models to be created in a timesaving way. With transferlearning, patterns that have been learned when solving previous problemsare used to solve new problems. In computer vision, transfer learning isusually implemented through the use of pre-trained models. A pre-trainedmodel is a model that was trained on a large benchmark dataset to solvea problem similar to a current problem to be solved. It is commonpractice to import and use models from published literature (e.g. VGG,Inception, MobileNet). A notable aspect of such deep learning models isthat they can automatically learn hierarchical feature representations.This means that features computed by the first layer are general and canbe reused in different problem domains, while features computed by thelast layer are specific and depend on the chosen dataset and task.

In a conventional CNN, the convolutional base 60 includes one or morelower layer, i.e. one or more layers at or close to the input, that areconfigured to perform low level feature extraction from input imagedata. In CNNs embodying the invention, the convolutional base 60comprises a layer 64 comprising a set of RGC models M. The RGC modelsmay be the same as or similar to the models M1 to Mn as described aboveand may be obtained in the same way. In comparison with a conventionalCNN, the low level feature extractor(s) of the convolutional base 60 arereplaced by a layer of RGC models M. In preferred embodiments, the RGCmodel layer 64 is the first layer of the convolutional base 60. An Image66 provided to the CNN is processed by the RGC model layer 64 whichextracts general image features from the input image. The extractedfeatures are provided to the subsequent parts of the CNN and processedin conventional manner. The RGC model layer 64 may extract features bycreating a multi-channel retina model image N_(G), as described above.

Advantageously, the classifier part 62, and the mid- and high-levelfeature layers of the convolutional base 60 may be conventional and sothe specialised features of the CNN are retained along with theproperties of the biological retina.

A standard deep-learning network D that produces a target output Tfrom astandard intensity image G as input may be defined as:

T=D(G)   (4)

where the deep network D is composed of the components D ({C_(l), c_(m),c_(h), t}, G) where c_(l), c_(m), and c_(h) correspond to the low-, mid-and high-level feature layers respectively in the convolutional base 60and t corresponds to the trainable classifier 62. In preferredembodiments, the low-level feature layer C/ is replaced with a set ofM^(s) _(n) RGC models such that:

D({M^(s) _(n), c_(m), c_(h), t}, G)   (5)

It will be apparent from the foregoing that embodiments of the inventionencode features in a digital image using biologically derivedcomputational models for subsequent learning processes, includingtraining and classification processes. The models encapsulate theprocessing within an RGC's neural structure, bypassing the need forcomplete understanding of biological system but maintaining theprocessing ability. In addition, low processing requirements makehardware implementations quick and low cost.

Embodiments of the invention may be implemented in any convenientmanner, for example in computer software and/or in hardware. Forexample, some aspects of the invention may be embodied as a machinelearning system, in particular a digital image classifier, in which casethe systemIciassifier may be implemented in hardware, for example by asuitably configured integrated circuit such as an ASIC or FPGA. Otheraspects of the invention may be implemented as a digital imageprocessor, or encoder, and may be implemented in hardware, for exampleby a suitably configured integrated circuit such as an ASIC or FPGA.Alternatively, machine learning systems, classifiers, processors orencoders embodying aspects of the invention may be implemented incomputer software, or by a combination of hardware and software as isconvenient.

The invention is not limited to the embodiment(s) described herein butcan be amended or modified without departing from the scope of thepresent invention.

1. A method of processing a digital image for use by a digital imageclassifier, the method comprising: processing said digital image witheach of a plurality of computational models of a retinal ganglion cell(RGC) to produce a respective set of digital image features; andcombining said sets of digital image features to produce a multi-channelretina model image.
 2. The method of claim 1, wherein at least some of,preferably all of, said RGC computational models are associated with arespective different receptive field (RF) of a retina.
 3. The method ofclaim 1, wherein said processing involves processing a respective partof said digital image with a respective one of said RGC computationalmodels.
 4. The method of claim 3, wherein at least some of, preferablyall of, said RGC computational models are associated with a respectivedifferent receptive field (RF) of a retina, and wherein said respectivepart of said digital image corresponds with the respective RF associatedwith the respective RGC computational model.
 5. The method of claim 1,wherein said processing involves convolving said digital image with eachof said RGC computational models.
 6. The method of claim 1, wherein atleast some of, preferably all of, said RGC computational models areassociated with a respective different receptive field (RF) of a retina,and wherein at least some of, preferably all of, said respectivereceptive fields (RFs) are overlapping with at least one other of saidrespective receptive fields (RFs).
 7. The method of claim 1, wherein atleast some of, preferably all of, said RGC computational models areassociated with a respective different receptive field (RF) of a retina,and wherein at least some of, preferably all of, said respectivereceptive fields (RFs) have a different size and/or a different shape.8. The method of claim 1, wherein each RGC computational model iscalculated from a dataset comprising input data and corresponding outputdata, wherein the input data comprises image data and the output datarepresents the response of an RGC to the image data.
 9. The method ofclaim 8, wherein at least some of said RGC computational models arederived from a different RGC and/or a different type of RGC.
 10. Themethod of claim 8, wherein said input data comprises data representing asequence of images, preferably a sequence of Gaussian images or asequence of checkerboard images.
 11. The method of clai 8, wherein eachRGC model is calculated from said dataset by reverse correlation and/orby machine learning.
 12. The method of claim 8, wherein each RGC modelcomprises a spike-triggered average (STA) derived from said dataset. 13.The method of claim 1, wherein at least some of, preferably all of, saidRGC computational models are associated with a respective differentreceptive field (RF) of a retina, and wherein each RGC computationalmodel corresponds to a different spatial portion of said image, andwherein, preferably, said plurality of RGC computational modelscollectively correspond to the whole image.
 14. The method of claim 13,wherein at least some of, preferably all of, said RGC computationalmodels correspond to a spatial portion of the image that overlaps withthe spatial image portion corresponding to at least one other of saidRGC computational models.
 15. (canceled)
 16. A method of training adigital image classifier, the method comprising processing digitalimages using a method of processing a digital image and providing thecorresponding multi-channel retina model images to the digital imageclassifier, wherein said method of processing a digital image comprises;processing said digital image with each of a plurality of computationalmodels of a retinal ganglion cell (RGC) to produce a respective set ofdigital image features; and combining said sets of digital imagefreatures to produce a multi-channel retina model image.
 17. The methodof claim 16, wherein said digital image classifier comprises anartificial neural network (ANN).
 18. A method of classifying a digitalimage using a digital image classifier, said method including providingsaid digital image classifier with a plurality of computational modelsof a retinal ganglion cell (RGC), processing said digital image witheach of a plurality of computational models of a retinal ganglion cell(RGC) to produce a respective set of digital image features; andcombining said sets of digital image features to produce a multi-channelretina model image.
 19. The method of claim 18, wherein said digitalimage classifier comprises an artificial neural network (ANN), andwherein providing said digital image classifier with a plurality ofcomputational models of a retinal ganglion cell (RGC) involves providingsaid RGC models in a first layer of said ANN.
 20. The method of claim18, wherein said digital image classifier comprises a convolutionalneural network (CNN), and wherein providing said digital imageclassifier with a plurality of computational models of a retinalganglion cell (RGC) involves providing said RGC models in aconvolutional base of said CNN. 21.-23. (canceled)